فهرست مطالب alireza amirteimoori

After assessing the efficiency of the units under evaluation, determining their ranking becomes a critical concern for decision-making. Ranking methodologies rely on specific unit inputs and outputs, but some variables affecting unit performance are not directly observable and are termed contextual variables. Thus, the primary objective of this research is to introduce a ranking methodology that incorporates these variables. To achieve this, a two-step approach is employed. Firstly, the Data Envelopment Analysis (DEA) technique calculates efficiency scores for each Decision-Making Unit (DMU), considering its unique inputs and outputs. Subsequently, the linear regression method is applied to assess the impact of contextual variables on efficiency. Finally, efficiency scores are modified by removing the impact of contextual variables, enabling unit ranking. To illustrate this proposed methodology, we conducted a performance evaluation of provincial gas companies in Iran. The evaluation covered four periods, from 2013 to 2016.

A significant part of each system is determining system efficiency to conduct future planning-associated operations. Data Envelopment Analysis (DEA) is often employed to measure system efficiency. The paper considers a bi-level structure and proposes a new non-radial method by generalizing Russell's model to measure system efficiency. Furthermore, data from 33 branches of an Iranian state bank in 2021 are investigated to present an example of the application. The results indicate that among 33 branches, only two branches are regarded as efficient at both leader and follower levels.

Since the healthcare system is one of the most important pillars of community health, and considering that providing healthcare services to the people is one of the elements of individual development in any country, attention and supervision of this sector can lead to development and social welfare. To ensure better and higher quality healthcare services, performance evaluation in the health sector plays a crucial role. In order to achieve this, proper and proportional use of existing facilities and assets is inevitable. In this study, by introducing an application in the field of healthcare systems, the educational hospitals of the country have been measured in terms of performance and their managerial ability has been calculated. Additionally, by identifying and introducing the impact of contextual variables on the performance of decision-making units, their efficiency has been assessed. For this purpose, data related to educational hospitals in 31 provinces of the country was collected, and then by identifying contextual variables and with the presence of undesirable factors, the efficiency was evaluated and the managerial ability of each was calculated. To reach this goal, in the first step, technical efficiency with the presence of undesirable factors was calculated using data envelopment analysis technique, and then the logarithm of technical efficiency obtained from the first stage was regressed on a set of contextual variables that affect hospital performance. In the next stage, managerial ability was extracted from the residual of the regression obtained from the previous stage. Finally, a unique ranking based on the managerial ability of each unit was provided. Ultimately, the results obtained were analyzed and examined in order to provide valuable suggestions for managers and more efficient management of the country's hospitals to maintain public health. According to the study, without considering contextual variables, 25 effective units were evaluated, but by applying the effect of contextual variables on the efficiency index, no unit becomes effective, proving the high impact of such indices on the performance of units. Additionally, in the ranking of units based on managerial ability, Lorestan province ranked first and Golestan province ranked last.

Cross-efficiency is a frequently used method for ranking decision-making units in Data Envelopment Analysis (DEA). A fundamental weakness of this method which has been quite problematic is the presence of multiple optimal weights along with selection of zero values by many of these multiple weights in calculating cross-efficiency. In the current paper it is tried to provide a method which through utilizing fair distribution of weights resolve the mentioned problems and in this way give more validity to the cross-efficiency method in raking decision-making units.

Due to the nonlinear and discrete nature of BCC (Banker, Charnes, and Cooper, [11]) models for determining the most efficient decision-making unit, it is practically impossible to evaluate the models' dual and, consequently, optimistic case. Thus, in this paper, the linear model with linear constraints proposed by Akhlaghi et al. [2] is used to investigate the dual equality of the model's robust problem and the optimistic case of the new model's dual under VRS uncertainty. The model proposed in this paper is novel in comparison to previous models because it solves the most efficient decision-making unit only once, without relying on uncertain data to determine its rank. The paper demonstrates how the proposed robust model can also ascertain the most efficient decision-making unit when uncertainty exists. Furthermore, the dual issues raised by robust counterparts in the new linear programming (LP) model are addressed to identify the most efficient decision-making unit. The robust counterpart is demonstrated to be equivalent to a linear program under interval uncertainty, and the dual of the robust counterpart is shown to be equal to the optimistic counterpart of the dual problem. Consequently, this study aims to demonstrate that the dual problem is equivalent to a decision-maker operating under optimal data, whereas the primal robust problem is equivalent to a decision-maker operating through the worst-case possible data scenario.

The aim of this research is to design scenarios based on cooperative game theory models to investigate the observed rewards and penalties resulting from knowledge sharing or non-sharing among companies. The research population includes managers and experts from 30 top private companies active in the field of oil and gas projects. The research methodology is based on Kumar's classification, which is applied, interpretive, exploratory, and includes both quantitative and qualitative aspects. This study underscores the importance of efficient knowledge sharing as a means to foster collaboration among investors in the portfolios of the National Iranian Oil Company to compete with foreign competitors during sanctions.Upon reviewing the results of four scenarios derived from the game, it is observed that the rewards perceived by companies from knowledge sharing depend on the behavior resulting from knowledge sharing by other companies. The more capable an organization is in the influential factors of knowledge transfer and higher innovation, the greater the returns companies will obtain through cooperation. Sensitivity analysis regarding the costs of project cooperation, which arises due to the difficulty of implementing joint projects, shows that as costs increase, companies shift from a cooperation strategy towards a retaliation strategy and then towards a non-cooperation strategy. These strategies are determined based on aligning the results of the profit equation designed with three interrelated cooperative games: the prisoner's dilemma, the hawk-dove game, and assurance game.

Supply chains often have different technologies. Additionally, organizations with multiple stages can evaluate their operational efficiency by analyzing scale elasticity, which helps determine if they are functioning optimally or if there is room for improvement. This evaluation allows for the identification of potential inefficiencies and opportunities for enhancement. Consequently, this research introduces a two-stage DEA-based approach with undesirable outputs to examine the scale elasticity of supply chains within meta and group frontiers. The measurement of group and meta performance of general systems and stages is conducted for this purpose. Moreover, the study addresses the scale elasticity of supply chains with undesirable outputs by considering the heterogeneity of technologies. To achieve this, the study focuses on the right and left scale elasticity of efficient general systems and each stage. A real-world application from the soft drink industry is provided to illustrate the proposed model. The results show the applicability of the introduced methodology.

Sensitivity analysis in optimization problems is important for managers and decision maker to introduce different strategies. Data Envelopment Analysis (DEA) is a method based on mathematical programming to evaluate the efficiency of a set of Decision-Making Units (DMUs). Due to the importance of sensitivity analysis in an optimization problem, a development of DEA model called inverse model in DEA is presented. The purpose of this model is to analyze the sensitivity of some inputs or outputs to changes in some other inputs or outputs of the unit under evaluation, provided that the amount of efficiency remains constant or improves at the discretion of the manager. In this research, for the first time, we introduce the inverse model in DEA with network structure. In fact, we examine the extent to which the input parameters are likely to change based on the presuppositions of the problem, for the output changes that are applied as the manager desires. One of the key points of this research is that to make the modeling more consistent with reality, the leader-follower method was used in estimating the parameters in the network. In addition, the opinions of the system manager and the decision maker, who have full control over the system under their management, are included in this modeling to estimate the desired values. Another feature of this modeling is the consideration of uncontrollable factors in the inverse model in DEA with network structure. Finally, using a numerical example, the results obtained are analyzed based on the proposed model.

The electricity industry plays a pivotal role in a country's economic growth and development. Therefore, it is imperative to assess its performance and identify the strengths and weaknesses of its different sectors, such as production, transmission, and distribution, to enhance economic growth in diverse areas. Given the significance of the transmission sector, this research focuses on analyzing and evaluating the performance of 16 regional electricity companies in Iran from 1390 to 1398, with the aim of comprehending the impact of contextual variables on efficiency. To achieve this, the study will utilize two techniques - Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS) - to determine the efficiency score and estimate the effect of contextual variables on efficiency, respectively. In the first stage, the DEA technique is employed to calculate the technical efficiency of each company, considering their specific inputs and outputs. In the second stage, the logarithm of the efficiency scores obtained is regressed on contextual variables to establish their effect on efficiency. The residual derived from the regression is referred to as managerial ability. Finally, the companies are ranked based on their modified efficiency after removing the impact of contextual variables.

Recognizing the significance of decisions and decision-making by the Islamic Parliament and their impact on human societies, it is crucial to evaluate the efficiency of different parliamentary terms, identify strengths and weaknesses, and determine influential factors for enhancing future performance. The reformation of the decision-making process is essential as it will result in sound decisions and elevate the political and economic conditions of the country. Thus, the objective of this research is to propose a two-stage data envelopment analysis (DEA) model to evaluate the performance of various periods of the Islamic Parliament (from the third to the tenth period) in terms of legislation and supervision. In the proposed approach, each term of the Islamic Parliament is considered a two-stage decision-making unit (DMU), with legislation as the first stage and supervision as the second stage. The results suggest that, generally, 50% of the periods are efficient, while the remaining 50% are inefficient. Specifically, Periods C, D, G, and H are identified as efficient periods across both dimensions. However, Period B, in terms of overall performance and the supervision dimension, and Period A, within the legislative dimension, are pinpointed as the most inefficient periods. Additionally, an analysis of the average efficiency scores in legislation and supervision underscores that, overall, the Islamic Parliament has demonstrated superior performance in legislation compared to supervision during various periods. This disparity in performance between the supervisory and legislative aspects has led to higher inefficiencies among the periods characterized as inefficient.

Imports and exports in all countries play an important role in economic growth. Therefore, choosing the right products will increase the country's competitiveness in world trade. Saffron is one of the most important and distinctive non-oil products of Iran for export. The purpose of this study is to predict the export of saffron through three machine learning algorithms and select a proper algorithm for predicting. The sample period of the forecasting models includes the data of Iran Saffron export from 2012 to 2019 which have been collected from the Iran Saffron Association. After performing the data preparation steps, the saffron export prediction was performed using three data mining algorithms including artificial neural network, deep learning, and gradient boost tree. To choose a better forecasting model, the validity of the model plays an essential role. Predictive validity of three designed models, using Absolute Error (Artificial neural network = 0.036, Network deep learning = 0.031, Gradiant boost tree = 0.047), R-squared (Artificial neural network = 0.045, Network deep learning = 0.044, Gradiant boost tree = 0.073) and Correlation (Artificial neural network = 0.95, Network deep learning = 0.98, Gradiant boost tree = 0.97) were measured. Based on the findings, all of these three designed models are accurate and their prediction error is very low and close to each other. However, with insignificant differences, the deep learning network has less error. The results could be useful for more accurate planning of saffron exports.

Evaluating the performance of countries in international markets and measuring how they utilize allocated resources provides a lucid picture of the state of business management in countries also assessing performance sustainability in international markets, is one of the important issues in predicting international trade. In international markets knowledge of countries' performance leads to the recognition of their economic and non-economic management statuses even more it is used to allocate financial support of global organization. In the present article, an attempt was made to design a model capable of examining the performance of nations in international markets by both economic and non-economic dimensions in the presence of undesirable factors and two preparation and exploitation stages of existing platforms and also assessing the sustainability of countries' performance in the studied period which is quite innovative. Then, data from 21 countries, from 2010 to 2017 were evaluated to test the model. The results indicated that the model is capable of examining the performance of countries and assess the sustainability of the countries over a period of time. According to the results of this model, an attempt was made to evaluate Iran's performance status among homogeneous countries and provide a roadmap for enhancing Iran's performance

Banks play an important role in the growth and development of any economy. A profitable banking system enhances economic stability and efficiency to mitigate the impacts of sudden macroeconomic shocks. To be more efficient and profitable, banks need to recognize the factors underpinning their performance. Accrued liabilities are one of the factors that hinder the profitability of banks. There are several methods to assess banks' profitability with their own pros and cons. Among them, data envelopment analysis (DEA) has been recommended as one of the most common approaches to evaluate different efficiencies including cost efficiency, revenue efficiency, technical efficiency, and finally profitability. The availability of prices/weights of inputs and outputs provides financial managers with significant information for evaluating efficiencies and assists them in decision-making and strategy development processes. This study mainly aims to analyze banks' profitability by considering accrued liabilities resulting from undesirable factors, for which relevant data were collected from 33 branches of a commercial bank in Gilan province, Iran based on managerial and weak disposability. The results illustrated that only three branches were graded one in three dimensions of efficiency, namely technical, cost and revenue, and profitability. Besides, it was suggested that the lack of these efficiencies was not correlated with the branches' profitability.

In traditional data envelopment analysis (DEA) models, the relative efficiency of decision making units (DMUs) is usually evaluated in a particular period of time such that the status of each measure from the input or output viewpoint is certain. However, in many applications, the performance of organizations should be measured over multiple periods of time while the status of some factors called “flexible measures” from the perspective of input or output is uncertain. The purpose of this study is to propose approaches to evaluate the efficiency of the multi-period systems where flexible measures are presented. For this reason, oriented and non-oriented DEA-based approaches from the standpoints of individual DMU and aggregate efficiencies are rendered to measure the overall and period efficiency of multi-period systems with flexible measures. Also, efficiency changes between two periods are estimated using the Malmquist productivity index (MPI). A dataset is provided to validate the proposed approach.

Cost efficiency in which cost coefficients are given for some inputs (cost coefficients can be different for disparate decision-making units (DMUs)) is one of the most important concepts in data envelopment analysis (DEA) to analyze the performance. Moreover, in some occasions, the cost performance and changes of input measures should be addressed while the convexity property is violated. Therefore, in this paper, first a DEA model is provided to assess cost efficiency based on the free disposal hull (FDH) model. Then, by considering cost and technical efficiencies achieved, a multi-objective problem called the inverse FDH cost model is presented to determine input values based on output changes while the cost and technical efficiency levels are preserved. The multi-objective problem is computed applying two approaches. Also, a dataset from the literature is presented to show the performance of the proposed method. For this purpose, we used the data of six banks in different countries. We added 2% to the outputs and analyzed the inputs with two models. In the first model, we used cost coefficients for weights, and in the second model, we used the same weights. Contrary to forecasts, some entries have decreased and others have increased. But from the results, we have noticed that the first model is more realistic because most of the solutions have increased in this model.

In the most investigations of sustainability, including environmental, social and economic issues, in addition to the desirable outputs, undesirable outputs are also presented, which is an obstacle to sustainable development. In this regard, the purpose of this paper is providing an approach based on Data Envelopment Analysis (DEA) with different forms of weak disposability of undesirable outputs to move towards sustainability.

Presenting a DEA-based model, the sustainability and performance of each dimension of sustainability are calculated simultaneously, while undesirable outputs are present with different forms of weak disposability. The sustainability performance of provincial gas companies is examined using the proposed technique.

The results show that the proposed method in the performance analysis of sustainability and its dimensions is efficient when undesirable outputs are presented.

Originality/Value:

 DEA provides a variety of disposability to minimize undesirable outputs and moves to optimize. In this study, an integrated approach with different forms of weak disposability is presented to analyze sustainability.

Congestion is one of the important concepts in data envelopment analysis that occurs when excessive inputs reduce the maximally possible outputs. Identification and elimination of congestion have a significant impact on reducing inputs along with increasing outputs. Hence, various studies have been conducted to detect and evaluate congestion. However, in today's world, no organization can achieve its final output with just one process of input. In other words, today's organizations have a network structure that consists of several subsections. Ignoring the existing influences among the subsections processes may lead to inadequate or even incorrect results for evaluating the congestion. While all of the existing methods only evaluate the congestion of each subsection or the whole system independently. Therefore, in this paper, the concept of congestion is developed for a specific and so practical case of network structure called “two-stage network structure”. This case of network structure consists of two series stage such that stage 1 consume some primary inputs to produce some intermediate outputs. In the following, the intermediate outputs are used as the inputs of stage 2 to produce the final output. Here, the concept of congestion is defined for systems with a two-stage structure. Then, to examine the congestion of each stage as well as the congestion of the whole system, a single linear programming model is proposed. The validity of the proposed model is investigated using several theorems and it is shown that the new definition is a generalization of the previous definitions of the congestion for the black-box systems. Finally, the proposed model is applied to a case study including 24 non-life insurance companies in Taiwan.

Estimating the revenue efficiency of entities under evaluation is one of the most important evaluations that can give valuable information about organizations provided that the output prices are known . In this research, a new definition of optimal scale size ( OSS ) based on maximizing the average revenue efficiency (ARE) is presented . Also , the ARE is defined in both convex and non - convex sets, which is independent of returns to scale and the assumption that the vector of input-output prices of units is uniform . Next, due to the presence of uncertain data in many real applications, the introduced ARE model is extended to evaluate systems with random inputs and outputs , and approaches are provided to calculate it . Finally , the proposed method is used in an experimental example and the ARE is calculated for a data set of postal areas in Iran .

Due to the complexity of the processes, analyzing the performance of the systems and examining the change in the scale of operations to improve productivity is a significant aspect. Therefore, in this research, a two-stage directional network data envelopment analysis approach is proposed to estimate the overall efficiency and the performance of each stage of two-stage processes, while undesirable outputs are produced in addition to desirable outputs. The proposed technology is based on the properties of the weak disposability of undesirable outputs and variable returns to scale. In addition, after presenting the response function, methods are proposed to calculate the right and left scale elasticities of overall efficient two-stage processes. In order to investigate the validity of the proposed method, a data set from 78 branches of the Iranian bank is used and their efficiency and scale elasticity are examined. The results show that the introduced method is useful and widely applicable to analyze the performance and scale of two-stage network systems in the presence of undesirable outputs.

Data envelopment analysis (DEA) is a nonparametric method for measuring the efficiency of decision-making units (DMUs) with multiple inputs and outputs. This research used the original DEA model and extended it to solve the DEA efficiency measurement problem, specifically for unseparated shared inputs. The consideration of this context aims to establish a new DEA approach to explore bank branch performance in different activities based on the optimal usage of unseparated shared inputs. In this study, in the first stage, the efficiency score is calculated from several activities using graph efficiency, and then, a maximum efficiency score pertaining to each DMU is applied to propose a new model. In the second stage, the efficiency score, which is calculated by the new approach on unseparated shared inputs, is defined as a new constraint based on shared inputs on the CCR model. This approach is implemented on the real data of 25 branches of a private bank in Iran. In fact, the efficiency of each branch is calculated, and enhancement guidelines are presented considering the three activities of production, electronic banking, and intermediation. Presenting one real efficiency score for each DMU, instead of the traditional efficiency score, leads to more robust decisions based on a more transparent performance evaluation in bank branches.

Cross-efficiency is a ranking technique based on the peer-evaluation that can increase the discriminating power between efficient decision-making units. This paper intends to assess the two-stage processes consisting of undesirable outputs by applying the cross-efficiency evaluation. Given undesirable outputs, the directional distance function under the weak disposability assumption is utilized. The proposed model under variable returns to scale is designed, which makes it different from the previous models. Furthermore, it can reduce the zero optimal coefficients. By measuring the inputs and outputs inefficiency, the whole system and each of its two stages rank, simultaneously. To analyze the suggested method, an application on the industrial productions of 30 regions of China is used.

In data envelopment analysis (DEA) models, the weak disposability axiom has been used for undesirable outputs such as environmental pollutants in many cases. Considering this axiom, different models are exhibited to assess the efficiency of units under evaluation. In this paper, we express duality in weak disposability models proposed by Kousmanen (2005) and Amirteimoori et al. (2017). In the following, economical interpretations of dual are represented in the abovementioned technologies. Also, projection points for inefficient units will be introduced; then, proposed methods will also be explained in an application that includes different designs for the production line of a company.

The electric power industry is one of the vital arteries contributing to the growth and development of countries and the electricity access index is one of the main components of assessing the industrial competitiveness in each country. Given the importance of this strategic industry, this study sought to develop an analytical-mathematical model for evaluating the efficiency of the power production, transmission, and distribution companies in the electric power industry of Iran.

In this study, the network data envelopment analysis (NDEA) approach with undesirable outputs was used. Additionally, a model was developed to evaluate the efficiency of the power production, transmission, and distribution companies in the electric power industry of Iran.

By solving the mathematical model used in this study, the efficiency of 43 governmental power plants, 16 regional electricity companies, and 39 power distribution companies in Iran were evaluated. The findings demonstrated that the average efficiency of the power production, transmission, and distribution companies in the Iranian electric power industry stands at 0.83, 0.6, and 0.71, respectively.

The results of this study indicated that the efficiency of the power transmission companies in the electric power industry of Iran is lower than those of the power production and distribution companies. The study also identified the main reason for the inefficiency of the power production, transmission, and distribution companies of Iran's electricity industry.

Data Envelopment Analysis (DEA) is a broad range of mathematical models for measuring the relative efficiency of a set of homogeneous decision units with similar inputs and outputs. Multiple models of data envelopment analysis render a set of weights for input and output variables of each decision unit to calculate the relative efficiency of those units based on them. The calculation of different weights for the same indices in a set of homogeneous decision units is not realistic. Therefore, the Common Set of Weights (CSW) method was used to solve this problem and the Bootstrap method was used to determine which common set of weights would minimize the number of efficient units. The rank of a unit can provide useful information to decision-makers on the optimal activities of decision units. The priority order of units defines the superiority of a unit in terms of efficiency and effectiveness over others. Calculating unit efficiency for data envelopment analysis models can be a good criterion for ranking one unit. However, the main problem arises when several efficient units all rank first. This study aimed at proposing a model for ranking efficient units using the Bootstrap method to determine the common set of weights in data envelopment analysis by finding a possible confidence interval for the weights using the Bootstrap method. This led to the estimation of a set of possible common weights for the data envelopment analysis. Efficient units were then identified and ranked based on these weights..

In recent decades, the topic of performance measurement has been one of the popular topics for large companies and manufacturers managers . The range - adjusted measurement ( RAM ) model in data envelopment analysis (DEA) is a non - radial model used to evaluate the performance of firms. Due to the presence of uncertain data in many investigations, a fully fuzzy range-adjusted measurement model with strong complementary slackness conditions to find efficient two-stage systems in a reference set is presented in this paper and it is used to evaluate airline . Given that we have a multi-objective network model of fully fuzzy range - adjusted measurement with strong complementary slackness conditions, the proposed model is solved using the lexicograph method . We also compare it with the existing fully fuzzy network DEA - range adjusted measurement model. Finally, we apply this model using the data of 14 Iranian airlines.